An unsupervised algorithm for arranging an image database as a binary tree is described. Tree nodes are associated with image subsets, maintaining the property that the similarity among the images associated with the children of a node is higher than the similarity among the images associated with the parent node. Experiments with datasets of hundreds and thousands of images show that shallow trees can produce clustering into "meaningful" classes. Visual-content search trees can be used to automate image retrieval by content, or help a human to interactively search for images.
Indexing images by trees of visual content
1998-01-01
842272 byte
Conference paper
Electronic Resource
English
Indexing Images by Trees of Visual Content
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